✨Controllable Memory Usage: Balancing Anchoring and Innovation in Long-Term Human-Agent Interaction
📝 Summary:
This paper presents SteeM, a framework for dynamically regulating memory reliance in LLM agents. It allows users to balance innovation with historical fidelity, overcoming the all-or-nothing problem of memory use. This approach outperforms conventional methods for personalized human-agent interac...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05107
• PDF: https://arxiv.org/pdf/2601.05107
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For more data science resources:
✓ https://xn--r1a.website/DataScienceT
#LLM #AI #HumanAgentInteraction #Memory #MachineLearning
📝 Summary:
This paper presents SteeM, a framework for dynamically regulating memory reliance in LLM agents. It allows users to balance innovation with historical fidelity, overcoming the all-or-nothing problem of memory use. This approach outperforms conventional methods for personalized human-agent interac...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05107
• PDF: https://arxiv.org/pdf/2601.05107
==================================
For more data science resources:
✓ https://xn--r1a.website/DataScienceT
#LLM #AI #HumanAgentInteraction #Memory #MachineLearning